495,743 research outputs found
The innovation network as a complex adaptive system: flexible multi-agent based modeling, simulation and evolutionary decision making
The literature rarely considers an innovation network as a complex adaptive system. In this paper, theories of complex adaptive systems research are employed to model and analyze intra-organization networks, inter-organization networks as well as their interaction mechanisms in the whole innovation context, with a conceptual framework proposed and presented. Flexible multi-agent based modeling, smart simulation, self-survival and adaptive intelligent software agents, expert systems, analytic hierarchy process, hybrid decision support approach, and statistical methods are integrated to deal with the innovation network problem and support evolutionary decision making in the open and dynamic environments
Wireless Sensor Networks and Real-Time Locating Systems to Fight against Maritime Piracy
There is a wide range of military and civil applications where Wireless Sensor Networks (WSNs) and Multi-Agent Systems (MASs) can be used for providing context-awareness for troops and special corps. On the one hand, WSNs comprise an ideal technology to develop Real-Time Locating Systems (RTLSs) aimed at indoor environments, where existing global navigation satellite systems do not work properly. On the other hand, agent-based architectures allow building autonomous and robust systems that are capable of working on highly dynamic scenarios. This paper presents two piracy scenarios where the n-Core platform can be applied. n-Core is a hardware and software platform intended for developing and deploying easily and quickly a wide variety of WSNs applications based on the ZigBee standard. In the first scenario a RTLS is deployed to support boarding and rescue operations. In the second scenario a multi-agent system is proposed to detect the unloading of illegal traffic of merchandise at ports
Transfer Learning for Improving Model Predictions in Highly Configurable Software
Modern software systems are built to be used in dynamic environments using
configuration capabilities to adapt to changes and external uncertainties. In a
self-adaptation context, we are often interested in reasoning about the
performance of the systems under different configurations. Usually, we learn a
black-box model based on real measurements to predict the performance of the
system given a specific configuration. However, as modern systems become more
complex, there are many configuration parameters that may interact and we end
up learning an exponentially large configuration space. Naturally, this does
not scale when relying on real measurements in the actual changing environment.
We propose a different solution: Instead of taking the measurements from the
real system, we learn the model using samples from other sources, such as
simulators that approximate performance of the real system at low cost. We
define a cost model that transform the traditional view of model learning into
a multi-objective problem that not only takes into account model accuracy but
also measurements effort as well. We evaluate our cost-aware transfer learning
solution using real-world configurable software including (i) a robotic system,
(ii) 3 different stream processing applications, and (iii) a NoSQL database
system. The experimental results demonstrate that our approach can achieve (a)
a high prediction accuracy, as well as (b) a high model reliability.Comment: To be published in the proceedings of the 12th International
Symposium on Software Engineering for Adaptive and Self-Managing Systems
(SEAMS'17
Dynamic re-optimization techniques for stream processing engines and object stores
Large scale data storage and processing systems are strongly motivated by the need to store and analyze massive datasets. The complexity of a large class of these systems is rooted in their distributed nature, extreme scale, need for real-time response, and streaming nature. The use of these systems on multi-tenant, cloud environments with potential resource interference necessitates fine-grained monitoring and control. In this dissertation, we present efficient, dynamic techniques for re-optimizing stream-processing systems and transactional object-storage systems.^ In the context of stream-processing systems, we present VAYU, a per-topology controller. VAYU uses novel methods and protocols for dynamic, network-aware tuple-routing in the dataflow. We show that the feedback-driven controller in VAYU helps achieve high pipeline throughput over long execution periods, as it dynamically detects and diagnoses any pipeline-bottlenecks. We present novel heuristics to optimize overlays for group communication operations in the streaming model.^ In the context of object-storage systems, we present M-Lock, a novel lock-localization service for distributed transaction protocols on scale-out object stores to increase transaction throughput. Lock localization refers to dynamic migration and partitioning of locks across nodes in the scale-out store to reduce cross-partition acquisition of locks. The service leverages the observed object-access patterns to achieve lock-clustering and deliver high performance. We also present TransMR, a framework that uses distributed, transactional object stores to orchestrate and execute asynchronous components in amorphous data-parallel applications on scale-out architectures
Transactions in dynamic reactive environments
Most of today’s systems, especially when related to the Web or to multi-agent systems,
are not standalone or independent, but are part of a greater ecosystem, where they
need to interact with other entities, react to complex changes in the environment, and
act both over its own knowledge base and on the external environment itself. Moreover,
these systems are clearly not static, but are constantly evolving due to the execution of self updates or external actions. Whenever actions and updates are possible, the need to ensure properties regarding the outcome of performing such actions emerges. Originally purposed in the context of databases, transactions solve this problem by guaranteeing atomicity, consistency, isolation and durability of a special set of actions. However, current transaction solutions fail to guarantee such properties in dynamic environments, since they cannot combine transaction execution with reactive features, or with the execution of actions over domains that the system does not completely control (thus making rolling back a non-viable proposition). In this thesis, we investigate what and how transaction
properties can be ensured over these dynamic environments. To achieve this goal, we provide logic-based solutions, based on Transaction Logic, to precisely model and execute transactions in such environments, and where knowledge bases can be defined by
arbitrary logic theories.Fundação para a Ciência e a Tecnologia (FCT) - grant SFRH/BD/64038/2009, and conceived
within project ERRO (PTDC/EIA-CCO/121823/2010
Safe Connectivity Maintenance in Underactuated Multi-Agent Networks for Dynamic Oceanic Environments
Autonomous Multi-Agent Systems are increasingly being deployed in
environments where winds and ocean currents can exert a significant influence
on their dynamics. Recent work has developed powerful control policies for
single agents that can leverage flows to achieve their objectives in dynamic
environments. However, in the context of multi-agent systems, these flows can
cause agents to collide or drift apart and lose direct inter-agent
communications, especially when agents have low propulsion capabilities. To
address these challenges, we propose a Hierarchical Multi-Agent Control
approach that allows arbitrary single agent performance policies that are
unaware of other agents to be used in multi-agent systems, while ensuring safe
operation. We first develop a safety controller solely dedicated to avoiding
collisions and maintaining inter-agent communication. Subsequently, we design a
low-interference safe interaction (LISIC) policy that trades-off the
performance policy and the safety controller to ensure safe and optimal
operation. Specifically, when the agents are at an appropriate distance, LISIC
prioritizes the performance policy, while smoothly increasing the safety
controller when necessary. We prove that under mild assumptions on the flows
experienced by the agents our approach can guarantee safety. Additionally, we
demonstrate the effectiveness of our method in realistic settings through an
extensive empirical analysis with underactuated Autonomous Surface Vehicles
(ASV) operating in dynamical ocean currents where the assumptions do not always
hold.Comment: 8 pages, submitted to 2023 IEEE 62th Annual Conference on Decision
and Control (CDC) Nicolas Hoischen and Marius Wiggert contributed equally to
this wor
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